Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
| Period | From | To |
|---|---|---|
| I sem. | Oct 2, 2017 | Jan 31, 2018 |
| II sem. | Mar 1, 2018 | Jun 15, 2018 |
| Session | From | To |
|---|---|---|
| Sessione invernale d'esami | Feb 1, 2018 | Feb 28, 2018 |
| Sessione estiva d'esame | Jun 18, 2018 | Jul 31, 2018 |
| Sessione autunnale d'esame | Sep 3, 2018 | Sep 28, 2018 |
| Session | From | To |
|---|---|---|
| Sessione Estiva Lauree Magistrali | Jul 19, 2018 | Jul 19, 2018 |
| Sessione Autunnale Lauree Magistrali | Oct 18, 2018 | Oct 18, 2018 |
| Sessione Invernale Lauree Magistrali | Mar 21, 2019 | Mar 21, 2019 |
| Period | From | To |
|---|---|---|
| Christmas break | Dec 22, 2017 | Jan 7, 2018 |
| Easter break | Mar 30, 2018 | Apr 3, 2018 |
| Patron Saint Day | May 21, 2018 | May 21, 2018 |
| Vacanze estive | Aug 6, 2018 | Aug 19, 2018 |
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
Bloisi Domenico Daniele
domenico.bloisi@univr.it
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1° Year
| Modules | Credits | TAF | SSD |
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2° Year
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Computational analysis of biological structures and networks (2017/2018)
Teaching code
4S004551
Teacher
Coordinatore
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
I sem. dal Oct 2, 2017 al Jan 31, 2018.
Learning outcomes
The course is aimed at providing the theoretical and applicative basis of Pattern Recognition techniques for the computational analysis of biological objects with a complex structure (such as graphs, sequences, networks, strings and so on). In particular, the course introduces and discusses the most important computational techniques for the analysis of structured data, with particular emphasis on the representation and on the generative and discriminative approaches.
At the end of the course, the students will be able to analyse a biological problem, involving complex and structured biological data, from a Pattern Recognition perspective; the will also have the skills needed to study, invent, develop and implement the different components of a Pattern Recognition System for biological structured data.
Program
CHAPTER 1 Basic Pattern Recognition concepts and introduction to structured data
CHAPTER 2. Representation of structured data
- Advanced dimensionality reduction techniques
- The Bag of words representation
- The dissimilarity-based representation
CHAPTER 3. Models for structured data
- Generative models
- Bayes Networks
- Learning and inference
CHAPTER 4. Kernels for structured data
- Support Vector Machines e kernel
- Kernels for structured data
CHAPTER 5. Deep Learning
Reference books:
R. Duda, P. Hart, D. Stork Pattern Classification. Wiley, 2001
P. Baldi, S. Brunak, Bioinformatics, The Machine Learning Approach. MIT Press, 2001
C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
Examination Methods
The exam is aimed at the verification of the following skills:
- capability of clearly and concisely describe the different components of a Pattern Recognition System for structured data
- capability of analise, understand and describe a Pattern Recognition system (or a given part of it) relative to a biological problem which involves structured data
The exam consists of two parts
i) a written exam containing questions on topics presented during the course (15 points available). The written part is passed is the grade is greater or equal to 8.
ii) an oral presentation of a scientific paper published in relevant bioinformatics journals or conferences on a given argument (decided during the course). The paper is chosen by the candidate and approved by the instructor (15 points available).
The two parts of the exam can be passed separately: the final grade is the sum of the two grades.
The total exam is passed if the final grade is greater or equal to 18. Each evaluation is maintained valid for the whole academic year.
Teaching materials
-
Assigned Papers
(pdf, it, 34 KB, 14/11/18)
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Assigned Papers (aa 2016-2017)
(pdf, it, 47 KB, 05/01/18)
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Instructions for exams
(pdf, it, 62 KB, 16/11/17)
Type D and Type F activities
Modules not yet included
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.
Further services
I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.
Graduation
Attendance
As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.Please refer to the Crisis Unit's latest updates for the mode of teaching.
